R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(9.4
+ ,0.5
+ ,5.1
+ ,-1.0
+ ,2504.7
+ ,9.4
+ ,0.8
+ ,5.0
+ ,3.0
+ ,2661.4
+ ,9.5
+ ,1.0
+ ,5.0
+ ,2.0
+ ,2880.4
+ ,9.5
+ ,1.3
+ ,5.1
+ ,3.0
+ ,3064.4
+ ,9.4
+ ,1.3
+ ,5.0
+ ,5.0
+ ,3141.1
+ ,9.4
+ ,1.2
+ ,4.9
+ ,5.0
+ ,3327.7
+ ,9.3
+ ,1.2
+ ,4.8
+ ,3.0
+ ,3565.0
+ ,9.4
+ ,1.0
+ ,4.5
+ ,2.0
+ ,3403.1
+ ,9.4
+ ,0.8
+ ,4.3
+ ,1.0
+ ,3149.9
+ ,9.2
+ ,0.7
+ ,4.3
+ ,-4.0
+ ,3006.8
+ ,9.1
+ ,0.6
+ ,4.2
+ ,1.0
+ ,3230.7
+ ,9.1
+ ,0.7
+ ,4.0
+ ,1.0
+ ,3361.1
+ ,9.1
+ ,1.0
+ ,3.8
+ ,6.0
+ ,3484.7
+ ,9.0
+ ,1.0
+ ,4.1
+ ,3.0
+ ,3411.1
+ ,9.0
+ ,1.3
+ ,4.2
+ ,2.0
+ ,3288.2
+ ,8.9
+ ,1.1
+ ,4.0
+ ,2.0
+ ,3280.4
+ ,8.8
+ ,0.8
+ ,4.3
+ ,2.0
+ ,3174.0
+ ,8.7
+ ,0.7
+ ,4.7
+ ,-8.0
+ ,3165.3
+ ,8.5
+ ,0.7
+ ,5.0
+ ,0.0
+ ,3092.7
+ ,8.3
+ ,0.9
+ ,5.1
+ ,-2.0
+ ,3053.1
+ ,8.1
+ ,1.3
+ ,5.4
+ ,3.0
+ ,3182.0
+ ,7.9
+ ,1.4
+ ,5.4
+ ,5.0
+ ,2999.9
+ ,7.8
+ ,1.6
+ ,5.4
+ ,8.0
+ ,3249.6
+ ,7.6
+ ,2.1
+ ,5.5
+ ,8.0
+ ,3210.5
+ ,7.4
+ ,0.3
+ ,5.8
+ ,9.0
+ ,3030.3
+ ,7.2
+ ,2.1
+ ,5.7
+ ,11.0
+ ,2803.5
+ ,7.0
+ ,2.5
+ ,5.5
+ ,13.0
+ ,2767.6
+ ,7.0
+ ,2.3
+ ,5.6
+ ,12.0
+ ,2882.6
+ ,6.8
+ ,2.4
+ ,5.6
+ ,13.0
+ ,2863.4
+ ,6.8
+ ,3.0
+ ,5.5
+ ,15.0
+ ,2897.1
+ ,6.7
+ ,1.7
+ ,5.5
+ ,13.0
+ ,3012.6
+ ,6.8
+ ,3.5
+ ,5.7
+ ,16.0
+ ,3143.0
+ ,6.7
+ ,4.0
+ ,5.6
+ ,10.0
+ ,3032.9
+ ,6.7
+ ,3.7
+ ,5.6
+ ,14.0
+ ,3045.8
+ ,6.7
+ ,3.7
+ ,5.4
+ ,14.0
+ ,3110.5
+ ,6.5
+ ,3.0
+ ,5.2
+ ,15.0
+ ,3013.2
+ ,6.3
+ ,2.7
+ ,5.1
+ ,13.0
+ ,2987.1
+ ,6.3
+ ,2.5
+ ,5.1
+ ,8.0
+ ,2995.6
+ ,6.3
+ ,2.2
+ ,5.0
+ ,7.0
+ ,2833.2
+ ,6.5
+ ,2.9
+ ,5.3
+ ,3.0
+ ,2849.0
+ ,6.6
+ ,3.1
+ ,5.4
+ ,3.0
+ ,2794.8
+ ,6.5
+ ,3.0
+ ,5.3
+ ,4.0
+ ,2845.3
+ ,6.3
+ ,2.8
+ ,5.1
+ ,4.0
+ ,2915.0
+ ,6.3
+ ,2.5
+ ,5.0
+ ,0.0
+ ,2892.6
+ ,6.5
+ ,1.9
+ ,5.0
+ ,-4.0
+ ,2604.4
+ ,7.0
+ ,1.9
+ ,4.6
+ ,-14.0
+ ,2641.7
+ ,7.1
+ ,1.8
+ ,4.8
+ ,-18.0
+ ,2659.8
+ ,7.3
+ ,2.0
+ ,5.1
+ ,-8.0
+ ,2638.5
+ ,7.3
+ ,2.6
+ ,5.1
+ ,-1.0
+ ,2720.3
+ ,7.4
+ ,2.5
+ ,5.1
+ ,1.0
+ ,2745.9
+ ,7.4
+ ,2.5
+ ,5.4
+ ,2.0
+ ,2735.7
+ ,7.3
+ ,1.6
+ ,5.3
+ ,0.0
+ ,2811.7
+ ,7.4
+ ,1.4
+ ,5.3
+ ,1.0
+ ,2799.4
+ ,7.5
+ ,0.8
+ ,5.1
+ ,0.0
+ ,2555.3
+ ,7.7
+ ,1.1
+ ,4.9
+ ,-1.0
+ ,2305.0
+ ,7.7
+ ,1.3
+ ,4.7
+ ,-3.0
+ ,2215.0
+ ,7.7
+ ,1.2
+ ,4.4
+ ,-3.0
+ ,2065.8
+ ,7.7
+ ,1.3
+ ,4.6
+ ,-3.0
+ ,1940.5
+ ,7.7
+ ,1.1
+ ,4.5
+ ,-4.0
+ ,2042.0
+ ,7.8
+ ,1.3
+ ,4.2
+ ,-8.0
+ ,1995.4
+ ,8.0
+ ,1.2
+ ,4.0
+ ,-9.0
+ ,1946.8
+ ,8.1
+ ,1.6
+ ,3.9
+ ,-13.0
+ ,1765.9
+ ,8.1
+ ,1.7
+ ,4.1
+ ,-18.0
+ ,1635.3
+ ,8.2
+ ,1.5
+ ,4.1
+ ,-11.0
+ ,1833.4
+ ,8.2
+ ,0.9
+ ,3.7
+ ,-9.0
+ ,1910.4
+ ,8.2
+ ,1.5
+ ,3.8
+ ,-10.0
+ ,1959.7
+ ,8.1
+ ,1.4
+ ,4.1
+ ,-13.0
+ ,1969.6
+ ,8.1
+ ,1.6
+ ,4.1
+ ,-11.0
+ ,2061.4
+ ,8.2
+ ,1.7
+ ,4.0
+ ,-5.0
+ ,2093.5
+ ,8.3
+ ,1.4
+ ,4.3
+ ,-15.0
+ ,2120.9
+ ,8.3
+ ,1.8
+ ,4.4
+ ,-6.0
+ ,2174.6
+ ,8.4
+ ,1.7
+ ,4.2
+ ,-6.0
+ ,2196.7
+ ,8.5
+ ,1.4
+ ,4.2
+ ,-3.0
+ ,2350.4
+ ,8.5
+ ,1.2
+ ,4.0
+ ,-1.0
+ ,2440.3
+ ,8.4
+ ,1.0
+ ,4.0
+ ,-3.0
+ ,2408.6
+ ,8.0
+ ,1.7
+ ,4.3
+ ,-4.0
+ ,2472.8
+ ,7.9
+ ,2.4
+ ,4.4
+ ,-6.0
+ ,2407.6
+ ,8.1
+ ,2.0
+ ,4.4
+ ,0.0
+ ,2454.6
+ ,8.5
+ ,2.1
+ ,4.3
+ ,-4.0
+ ,2448.1
+ ,8.8
+ ,2.0
+ ,4.1
+ ,-2.0
+ ,2497.8
+ ,8.8
+ ,1.8
+ ,4.1
+ ,-2.0
+ ,2645.6
+ ,8.6
+ ,2.7
+ ,3.9
+ ,-6.0
+ ,2756.8
+ ,8.3
+ ,2.3
+ ,3.8
+ ,-7.0
+ ,2849.3
+ ,8.3
+ ,1.9
+ ,3.7
+ ,-6.0
+ ,2921.4
+ ,8.3
+ ,2.0
+ ,3.5
+ ,-6.0
+ ,2981.9
+ ,8.4
+ ,2.3
+ ,3.7
+ ,-3.0
+ ,3080.6
+ ,8.4
+ ,2.8
+ ,3.7
+ ,-2.0
+ ,3106.2
+ ,8.5
+ ,2.4
+ ,3.5
+ ,-5.0
+ ,3119.3
+ ,8.6
+ ,2.3
+ ,3.3
+ ,-11.0
+ ,3061.3
+ ,8.6
+ ,2.7
+ ,3.2
+ ,-11.0
+ ,3097.3
+ ,8.6
+ ,2.7
+ ,3.3
+ ,-11.0
+ ,3161.7
+ ,8.6
+ ,2.9
+ ,3.1
+ ,-10.0
+ ,3257.2
+ ,8.6
+ ,3.0
+ ,3.2
+ ,-14.0
+ ,3277.0
+ ,8.5
+ ,2.2
+ ,3.4
+ ,-8.0
+ ,3295.3
+ ,8.4
+ ,2.3
+ ,3.5
+ ,-9.0
+ ,3364.0
+ ,8.4
+ ,2.8
+ ,3.3
+ ,-5.0
+ ,3494.2
+ ,8.4
+ ,2.8
+ ,3.5
+ ,-1.0
+ ,3667.0
+ ,8.5
+ ,2.8
+ ,3.5
+ ,-2.0
+ ,3813.1
+ ,8.5
+ ,2.2
+ ,3.8
+ ,-5.0
+ ,3918.0
+ ,8.6
+ ,2.6
+ ,4.0
+ ,-4.0
+ ,3895.5
+ ,8.6
+ ,2.8
+ ,4.0
+ ,-6.0
+ ,3801.1
+ ,8.4
+ ,2.5
+ ,4.1
+ ,-2.0
+ ,3570.1
+ ,8.2
+ ,2.4
+ ,4.0
+ ,-2.0
+ ,3701.6
+ ,8.0
+ ,2.3
+ ,3.8
+ ,-2.0
+ ,3862.3
+ ,8.0
+ ,1.9
+ ,3.7
+ ,-2.0
+ ,3970.1
+ ,8.0
+ ,1.7
+ ,3.8
+ ,2.0
+ ,4138.5
+ ,8.0
+ ,2.0
+ ,3.7
+ ,1.0
+ ,4199.8
+ ,7.9
+ ,2.1
+ ,4.0
+ ,-8.0
+ ,4290.9
+ ,7.9
+ ,1.7
+ ,4.2
+ ,-1.0
+ ,4443.9
+ ,7.8
+ ,1.8
+ ,4.0
+ ,1.0
+ ,4502.6
+ ,7.8
+ ,1.8
+ ,4.1
+ ,-1.0
+ ,4357.0
+ ,8.0
+ ,1.8
+ ,4.2
+ ,2.0
+ ,4591.3
+ ,7.8
+ ,1.3
+ ,4.5
+ ,2.0
+ ,4697.0
+ ,7.4
+ ,1.3
+ ,4.6
+ ,1.0
+ ,4621.4
+ ,7.2
+ ,1.3
+ ,4.5
+ ,-1.0
+ ,4562.8
+ ,7.0
+ ,1.2
+ ,4.5
+ ,-2.0
+ ,4202.5
+ ,7.0
+ ,1.4
+ ,4.5
+ ,-2.0
+ ,4296.5
+ ,7.2
+ ,2.2
+ ,4.4
+ ,-1.0
+ ,4435.2
+ ,7.2
+ ,2.9
+ ,4.3
+ ,-8.0
+ ,4105.2
+ ,7.2
+ ,3.1
+ ,4.5
+ ,-4.0
+ ,4116.7
+ ,7.0
+ ,3.5
+ ,4.1
+ ,-6.0
+ ,3844.5
+ ,6.9
+ ,3.6
+ ,4.1
+ ,-3.0
+ ,3721.0
+ ,6.8
+ ,4.4
+ ,4.3
+ ,-3.0
+ ,3674.4
+ ,6.8
+ ,4.1
+ ,4.4
+ ,-7.0
+ ,3857.6
+ ,6.8
+ ,5.1
+ ,4.7
+ ,-9.0
+ ,3801.1
+ ,6.9
+ ,5.8
+ ,5.0
+ ,-11.0
+ ,3504.4
+ ,7.2
+ ,5.9
+ ,4.7
+ ,-13.0
+ ,3032.6
+ ,7.2
+ ,5.4
+ ,4.5
+ ,-11.0
+ ,3047.0
+ ,7.2
+ ,5.5
+ ,4.5
+ ,-9.0
+ ,2962.3
+ ,7.1
+ ,4.8
+ ,4.5
+ ,-17.0
+ ,2197.8
+ ,7.2
+ ,3.2
+ ,5.5
+ ,-22.0
+ ,2014.5
+ ,7.3
+ ,2.7
+ ,4.5
+ ,-25.0
+ ,1862.8
+ ,7.5
+ ,2.1
+ ,4.4
+ ,-20.0
+ ,1905.4
+ ,7.6
+ ,1.9
+ ,4.2
+ ,-24.0
+ ,1811.0
+ ,7.7
+ ,0.6
+ ,3.9
+ ,-24.0
+ ,1670.1
+ ,7.7
+ ,0.7
+ ,3.9
+ ,-22.0
+ ,1864.4
+ ,7.7
+ ,-0.2
+ ,4.2
+ ,-19.0
+ ,2052.0
+ ,7.8
+ ,-1.0
+ ,4.0
+ ,-18.0
+ ,2029.6
+ ,8.0
+ ,-1.7
+ ,3.8
+ ,-17.0
+ ,2070.8
+ ,8.1
+ ,-0.7
+ ,3.7
+ ,-11.0
+ ,2293.4
+ ,8.1
+ ,-1.0
+ ,3.7
+ ,-11.0
+ ,2443.3
+ ,8.0
+ ,-0.9
+ ,3.7
+ ,-12.0
+ ,2513.2
+ ,8.1
+ ,0.0
+ ,3.7
+ ,-10.0
+ ,2466.9
+ ,8.2
+ ,0.3
+ ,3.7
+ ,-15.0
+ ,2502.7
+ ,8.3
+ ,0.8
+ ,3.8
+ ,-15.0
+ ,2539.9
+ ,8.4
+ ,0.8
+ ,3.7
+ ,-15.0
+ ,2482.6
+ ,8.4
+ ,1.9
+ ,3.5
+ ,-13.0
+ ,2626.2
+ ,8.4
+ ,2.1
+ ,3.5
+ ,-8.0
+ ,2656.3
+ ,8.5
+ ,2.5
+ ,3.1
+ ,-13.0
+ ,2446.7
+ ,8.5
+ ,2.7
+ ,3.4
+ ,-9.0
+ ,2467.4
+ ,8.6
+ ,2.4
+ ,3.3
+ ,-7.0
+ ,2462.3
+ ,8.6
+ ,2.4
+ ,2.8
+ ,-4.0
+ ,2504.6
+ ,8.5
+ ,2.9
+ ,3.2
+ ,-4.0
+ ,2579.4
+ ,8.5
+ ,3.1
+ ,3.4
+ ,-2.0
+ ,2649.2)
+ ,dim=c(5
+ ,154)
+ ,dimnames=list(c('werkloosheid'
+ ,'hicp'
+ ,'rente'
+ ,'consumer'
+ ,'bel20')
+ ,1:154))
> y <- array(NA,dim=c(5,154),dimnames=list(c('werkloosheid','hicp','rente','consumer','bel20'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
werkloosheid hicp rente consumer bel20
1 9.4 0.5 5.1 -1 2504.7
2 9.4 0.8 5.0 3 2661.4
3 9.5 1.0 5.0 2 2880.4
4 9.5 1.3 5.1 3 3064.4
5 9.4 1.3 5.0 5 3141.1
6 9.4 1.2 4.9 5 3327.7
7 9.3 1.2 4.8 3 3565.0
8 9.4 1.0 4.5 2 3403.1
9 9.4 0.8 4.3 1 3149.9
10 9.2 0.7 4.3 -4 3006.8
11 9.1 0.6 4.2 1 3230.7
12 9.1 0.7 4.0 1 3361.1
13 9.1 1.0 3.8 6 3484.7
14 9.0 1.0 4.1 3 3411.1
15 9.0 1.3 4.2 2 3288.2
16 8.9 1.1 4.0 2 3280.4
17 8.8 0.8 4.3 2 3174.0
18 8.7 0.7 4.7 -8 3165.3
19 8.5 0.7 5.0 0 3092.7
20 8.3 0.9 5.1 -2 3053.1
21 8.1 1.3 5.4 3 3182.0
22 7.9 1.4 5.4 5 2999.9
23 7.8 1.6 5.4 8 3249.6
24 7.6 2.1 5.5 8 3210.5
25 7.4 0.3 5.8 9 3030.3
26 7.2 2.1 5.7 11 2803.5
27 7.0 2.5 5.5 13 2767.6
28 7.0 2.3 5.6 12 2882.6
29 6.8 2.4 5.6 13 2863.4
30 6.8 3.0 5.5 15 2897.1
31 6.7 1.7 5.5 13 3012.6
32 6.8 3.5 5.7 16 3143.0
33 6.7 4.0 5.6 10 3032.9
34 6.7 3.7 5.6 14 3045.8
35 6.7 3.7 5.4 14 3110.5
36 6.5 3.0 5.2 15 3013.2
37 6.3 2.7 5.1 13 2987.1
38 6.3 2.5 5.1 8 2995.6
39 6.3 2.2 5.0 7 2833.2
40 6.5 2.9 5.3 3 2849.0
41 6.6 3.1 5.4 3 2794.8
42 6.5 3.0 5.3 4 2845.3
43 6.3 2.8 5.1 4 2915.0
44 6.3 2.5 5.0 0 2892.6
45 6.5 1.9 5.0 -4 2604.4
46 7.0 1.9 4.6 -14 2641.7
47 7.1 1.8 4.8 -18 2659.8
48 7.3 2.0 5.1 -8 2638.5
49 7.3 2.6 5.1 -1 2720.3
50 7.4 2.5 5.1 1 2745.9
51 7.4 2.5 5.4 2 2735.7
52 7.3 1.6 5.3 0 2811.7
53 7.4 1.4 5.3 1 2799.4
54 7.5 0.8 5.1 0 2555.3
55 7.7 1.1 4.9 -1 2305.0
56 7.7 1.3 4.7 -3 2215.0
57 7.7 1.2 4.4 -3 2065.8
58 7.7 1.3 4.6 -3 1940.5
59 7.7 1.1 4.5 -4 2042.0
60 7.8 1.3 4.2 -8 1995.4
61 8.0 1.2 4.0 -9 1946.8
62 8.1 1.6 3.9 -13 1765.9
63 8.1 1.7 4.1 -18 1635.3
64 8.2 1.5 4.1 -11 1833.4
65 8.2 0.9 3.7 -9 1910.4
66 8.2 1.5 3.8 -10 1959.7
67 8.1 1.4 4.1 -13 1969.6
68 8.1 1.6 4.1 -11 2061.4
69 8.2 1.7 4.0 -5 2093.5
70 8.3 1.4 4.3 -15 2120.9
71 8.3 1.8 4.4 -6 2174.6
72 8.4 1.7 4.2 -6 2196.7
73 8.5 1.4 4.2 -3 2350.4
74 8.5 1.2 4.0 -1 2440.3
75 8.4 1.0 4.0 -3 2408.6
76 8.0 1.7 4.3 -4 2472.8
77 7.9 2.4 4.4 -6 2407.6
78 8.1 2.0 4.4 0 2454.6
79 8.5 2.1 4.3 -4 2448.1
80 8.8 2.0 4.1 -2 2497.8
81 8.8 1.8 4.1 -2 2645.6
82 8.6 2.7 3.9 -6 2756.8
83 8.3 2.3 3.8 -7 2849.3
84 8.3 1.9 3.7 -6 2921.4
85 8.3 2.0 3.5 -6 2981.9
86 8.4 2.3 3.7 -3 3080.6
87 8.4 2.8 3.7 -2 3106.2
88 8.5 2.4 3.5 -5 3119.3
89 8.6 2.3 3.3 -11 3061.3
90 8.6 2.7 3.2 -11 3097.3
91 8.6 2.7 3.3 -11 3161.7
92 8.6 2.9 3.1 -10 3257.2
93 8.6 3.0 3.2 -14 3277.0
94 8.5 2.2 3.4 -8 3295.3
95 8.4 2.3 3.5 -9 3364.0
96 8.4 2.8 3.3 -5 3494.2
97 8.4 2.8 3.5 -1 3667.0
98 8.5 2.8 3.5 -2 3813.1
99 8.5 2.2 3.8 -5 3918.0
100 8.6 2.6 4.0 -4 3895.5
101 8.6 2.8 4.0 -6 3801.1
102 8.4 2.5 4.1 -2 3570.1
103 8.2 2.4 4.0 -2 3701.6
104 8.0 2.3 3.8 -2 3862.3
105 8.0 1.9 3.7 -2 3970.1
106 8.0 1.7 3.8 2 4138.5
107 8.0 2.0 3.7 1 4199.8
108 7.9 2.1 4.0 -8 4290.9
109 7.9 1.7 4.2 -1 4443.9
110 7.8 1.8 4.0 1 4502.6
111 7.8 1.8 4.1 -1 4357.0
112 8.0 1.8 4.2 2 4591.3
113 7.8 1.3 4.5 2 4697.0
114 7.4 1.3 4.6 1 4621.4
115 7.2 1.3 4.5 -1 4562.8
116 7.0 1.2 4.5 -2 4202.5
117 7.0 1.4 4.5 -2 4296.5
118 7.2 2.2 4.4 -1 4435.2
119 7.2 2.9 4.3 -8 4105.2
120 7.2 3.1 4.5 -4 4116.7
121 7.0 3.5 4.1 -6 3844.5
122 6.9 3.6 4.1 -3 3721.0
123 6.8 4.4 4.3 -3 3674.4
124 6.8 4.1 4.4 -7 3857.6
125 6.8 5.1 4.7 -9 3801.1
126 6.9 5.8 5.0 -11 3504.4
127 7.2 5.9 4.7 -13 3032.6
128 7.2 5.4 4.5 -11 3047.0
129 7.2 5.5 4.5 -9 2962.3
130 7.1 4.8 4.5 -17 2197.8
131 7.2 3.2 5.5 -22 2014.5
132 7.3 2.7 4.5 -25 1862.8
133 7.5 2.1 4.4 -20 1905.4
134 7.6 1.9 4.2 -24 1811.0
135 7.7 0.6 3.9 -24 1670.1
136 7.7 0.7 3.9 -22 1864.4
137 7.7 -0.2 4.2 -19 2052.0
138 7.8 -1.0 4.0 -18 2029.6
139 8.0 -1.7 3.8 -17 2070.8
140 8.1 -0.7 3.7 -11 2293.4
141 8.1 -1.0 3.7 -11 2443.3
142 8.0 -0.9 3.7 -12 2513.2
143 8.1 0.0 3.7 -10 2466.9
144 8.2 0.3 3.7 -15 2502.7
145 8.3 0.8 3.8 -15 2539.9
146 8.4 0.8 3.7 -15 2482.6
147 8.4 1.9 3.5 -13 2626.2
148 8.4 2.1 3.5 -8 2656.3
149 8.5 2.5 3.1 -13 2446.7
150 8.5 2.7 3.4 -9 2467.4
151 8.6 2.4 3.3 -7 2462.3
152 8.6 2.4 2.8 -4 2504.6
153 8.5 2.9 3.2 -4 2579.4
154 8.5 3.1 3.4 -2 2649.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) hicp rente consumer bel20
1.164e+01 -2.419e-01 -6.992e-01 2.044e-02 -4.544e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.32994 -0.37399 -0.02126 0.31995 1.81450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.164e+01 5.391e-01 21.598 < 2e-16 ***
hicp -2.419e-01 4.209e-02 -5.749 4.91e-08 ***
rente -6.992e-01 9.157e-02 -7.636 2.49e-12 ***
consumer 2.044e-02 8.003e-03 2.554 0.0116 *
bel20 -4.544e-05 8.208e-05 -0.554 0.5807
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6088 on 149 degrees of freedom
Multiple R-squared: 0.4399, Adjusted R-squared: 0.4248
F-statistic: 29.25 on 4 and 149 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.512818e-03 3.025635e-03 9.984872e-01
[2,] 1.854057e-04 3.708114e-04 9.998146e-01
[3,] 4.247456e-04 8.494913e-04 9.995753e-01
[4,] 3.042006e-04 6.084012e-04 9.996958e-01
[5,] 7.900818e-05 1.580164e-04 9.999210e-01
[6,] 1.800616e-05 3.601233e-05 9.999820e-01
[7,] 2.500703e-05 5.001405e-05 9.999750e-01
[8,] 2.207374e-05 4.414749e-05 9.999779e-01
[9,] 1.163396e-05 2.326792e-05 9.999884e-01
[10,] 1.434343e-04 2.868687e-04 9.998566e-01
[11,] 5.935817e-04 1.187163e-03 9.994064e-01
[12,] 2.400014e-02 4.800029e-02 9.759999e-01
[13,] 1.325299e-01 2.650599e-01 8.674701e-01
[14,] 5.067135e-01 9.865730e-01 4.932865e-01
[15,] 8.610832e-01 2.778336e-01 1.389168e-01
[16,] 9.428605e-01 1.142789e-01 5.713946e-02
[17,] 9.566545e-01 8.669108e-02 4.334554e-02
[18,] 9.883116e-01 2.337683e-02 1.168842e-02
[19,] 9.960299e-01 7.940163e-03 3.970081e-03
[20,] 9.972686e-01 5.462769e-03 2.731384e-03
[21,] 9.971275e-01 5.744998e-03 2.872499e-03
[22,] 9.968525e-01 6.294950e-03 3.147475e-03
[23,] 9.952490e-01 9.502014e-03 4.751007e-03
[24,] 9.969889e-01 6.022298e-03 3.011149e-03
[25,] 9.960196e-01 7.960748e-03 3.980374e-03
[26,] 9.942302e-01 1.153960e-02 5.769798e-03
[27,] 9.915633e-01 1.687345e-02 8.436726e-03
[28,] 9.878939e-01 2.421214e-02 1.210607e-02
[29,] 9.896001e-01 2.079972e-02 1.039986e-02
[30,] 9.970454e-01 5.909150e-03 2.954575e-03
[31,] 9.997482e-01 5.036367e-04 2.518183e-04
[32,] 9.999923e-01 1.531990e-05 7.659948e-06
[33,] 9.999966e-01 6.879492e-06 3.439746e-06
[34,] 9.999959e-01 8.106396e-06 4.053198e-06
[35,] 9.999971e-01 5.810220e-06 2.905110e-06
[36,] 9.999997e-01 5.976033e-07 2.988016e-07
[37,] 1.000000e+00 1.815920e-08 9.079600e-09
[38,] 1.000000e+00 1.198596e-09 5.992980e-10
[39,] 1.000000e+00 1.154203e-09 5.771014e-10
[40,] 1.000000e+00 2.196256e-09 1.098128e-09
[41,] 1.000000e+00 4.326089e-09 2.163044e-09
[42,] 1.000000e+00 5.983615e-09 2.991808e-09
[43,] 1.000000e+00 9.005405e-09 4.502703e-09
[44,] 1.000000e+00 1.149889e-08 5.749443e-09
[45,] 1.000000e+00 1.913477e-08 9.567383e-09
[46,] 1.000000e+00 2.995853e-08 1.497927e-08
[47,] 1.000000e+00 3.965113e-08 1.982557e-08
[48,] 1.000000e+00 7.682536e-08 3.841268e-08
[49,] 9.999999e-01 1.350866e-07 6.754330e-08
[50,] 9.999999e-01 1.761858e-07 8.809290e-08
[51,] 9.999999e-01 2.351638e-07 1.175819e-07
[52,] 9.999998e-01 3.350164e-07 1.675082e-07
[53,] 9.999998e-01 4.495636e-07 2.247818e-07
[54,] 9.999997e-01 6.436666e-07 3.218333e-07
[55,] 9.999997e-01 6.832966e-07 3.416483e-07
[56,] 9.999997e-01 5.901143e-07 2.950572e-07
[57,] 9.999996e-01 8.441506e-07 4.220753e-07
[58,] 9.999995e-01 1.092120e-06 5.460600e-07
[59,] 9.999991e-01 1.708382e-06 8.541911e-07
[60,] 9.999985e-01 2.949713e-06 1.474856e-06
[61,] 9.999976e-01 4.894786e-06 2.447393e-06
[62,] 9.999965e-01 7.056769e-06 3.528384e-06
[63,] 9.999968e-01 6.324502e-06 3.162251e-06
[64,] 9.999968e-01 6.493269e-06 3.246634e-06
[65,] 9.999960e-01 7.961413e-06 3.980706e-06
[66,] 9.999945e-01 1.095536e-05 5.477680e-06
[67,] 9.999907e-01 1.861978e-05 9.309891e-06
[68,] 9.999850e-01 2.999865e-05 1.499933e-05
[69,] 9.999748e-01 5.037133e-05 2.518566e-05
[70,] 9.999651e-01 6.976601e-05 3.488300e-05
[71,] 9.999476e-01 1.047130e-04 5.235648e-05
[72,] 9.999650e-01 6.997414e-05 3.498707e-05
[73,] 9.999856e-01 2.884240e-05 1.442120e-05
[74,] 9.999958e-01 8.419941e-06 4.209971e-06
[75,] 9.999980e-01 3.990062e-06 1.995031e-06
[76,] 9.999970e-01 6.092593e-06 3.046297e-06
[77,] 9.999955e-01 9.085524e-06 4.542762e-06
[78,] 9.999933e-01 1.348319e-05 6.741596e-06
[79,] 9.999900e-01 2.000906e-05 1.000453e-05
[80,] 9.999862e-01 2.752130e-05 1.376065e-05
[81,] 9.999788e-01 4.238712e-05 2.119356e-05
[82,] 9.999659e-01 6.824620e-05 3.412310e-05
[83,] 9.999431e-01 1.138242e-04 5.691210e-05
[84,] 9.999116e-01 1.768789e-04 8.843944e-05
[85,] 9.998545e-01 2.909834e-04 1.454917e-04
[86,] 9.997745e-01 4.510271e-04 2.255135e-04
[87,] 9.996833e-01 6.333501e-04 3.166751e-04
[88,] 9.995581e-01 8.838793e-04 4.419397e-04
[89,] 9.993237e-01 1.352629e-03 6.763144e-04
[90,] 9.990114e-01 1.977133e-03 9.885665e-04
[91,] 9.987135e-01 2.573066e-03 1.286533e-03
[92,] 9.990186e-01 1.962802e-03 9.814009e-04
[93,] 9.997103e-01 5.794491e-04 2.897245e-04
[94,] 9.999608e-01 7.835621e-05 3.917810e-05
[95,] 9.999856e-01 2.880536e-05 1.440268e-05
[96,] 9.999883e-01 2.342334e-05 1.171167e-05
[97,] 9.999853e-01 2.936924e-05 1.468462e-05
[98,] 9.999856e-01 2.871644e-05 1.435822e-05
[99,] 9.999868e-01 2.644647e-05 1.322323e-05
[100,] 9.999845e-01 3.101398e-05 1.550699e-05
[101,] 9.999870e-01 2.593659e-05 1.296830e-05
[102,] 9.999915e-01 1.705977e-05 8.529884e-06
[103,] 9.999908e-01 1.843241e-05 9.216203e-06
[104,] 9.999904e-01 1.928777e-05 9.643885e-06
[105,] 9.999964e-01 7.140986e-06 3.570493e-06
[106,] 9.999995e-01 1.089698e-06 5.448488e-07
[107,] 9.999997e-01 5.233503e-07 2.616751e-07
[108,] 9.999998e-01 4.821214e-07 2.410607e-07
[109,] 9.999997e-01 6.096181e-07 3.048090e-07
[110,] 9.999995e-01 9.450831e-07 4.725416e-07
[111,] 9.999992e-01 1.576166e-06 7.880831e-07
[112,] 9.999986e-01 2.804472e-06 1.402236e-06
[113,] 9.999983e-01 3.300173e-06 1.650087e-06
[114,] 9.999970e-01 5.978901e-06 2.989451e-06
[115,] 9.999985e-01 3.038409e-06 1.519204e-06
[116,] 9.999996e-01 7.755762e-07 3.877881e-07
[117,] 9.999998e-01 3.406913e-07 1.703456e-07
[118,] 9.999999e-01 2.196614e-07 1.098307e-07
[119,] 9.999998e-01 3.148763e-07 1.574382e-07
[120,] 9.999997e-01 6.134230e-07 3.067115e-07
[121,] 9.999997e-01 5.322535e-07 2.661268e-07
[122,] 1.000000e+00 1.908812e-08 9.544060e-09
[123,] 1.000000e+00 3.967859e-11 1.983929e-11
[124,] 1.000000e+00 9.738317e-11 4.869159e-11
[125,] 1.000000e+00 1.218348e-10 6.091738e-11
[126,] 1.000000e+00 4.622329e-10 2.311164e-10
[127,] 1.000000e+00 1.784814e-09 8.924070e-10
[128,] 1.000000e+00 9.139214e-09 4.569607e-09
[129,] 1.000000e+00 1.067895e-08 5.339473e-09
[130,] 1.000000e+00 8.656976e-09 4.328488e-09
[131,] 1.000000e+00 2.009169e-09 1.004584e-09
[132,] 1.000000e+00 1.332202e-08 6.661008e-09
[133,] 9.999999e-01 1.010109e-07 5.050545e-08
[134,] 9.999997e-01 6.831750e-07 3.415875e-07
[135,] 9.999976e-01 4.802186e-06 2.401093e-06
[136,] 9.999964e-01 7.101824e-06 3.550912e-06
[137,] 9.999971e-01 5.757241e-06 2.878621e-06
[138,] 9.999824e-01 3.518430e-05 1.759215e-05
[139,] 9.998281e-01 3.437576e-04 1.718788e-04
> postscript(file="/var/www/rcomp/tmp/1n2l81292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ft2t1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3ft2t1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/48ljv1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/58ljv1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5 6
1.577269285 1.505286785 1.684069115 1.814495152 1.607175538 1.521537471
7 8 9 10 11 12
1.403280906 1.258210174 1.078912834 0.950422696 0.664272430 0.554548714
13 14 15 16 17 18
0.390698874 0.558444595 0.715808408 0.427220242 0.459566489 0.819078000
19 20 21 22 23 24
0.662013645 0.619408933 0.629604465 0.404642265 0.303053912 0.292173960
25 26 27 28 29 30
-0.162198871 -0.047799863 -0.333377943 -0.286179106 -0.483297980 -0.447401719
31 32 33 34 35 36
-0.815805169 -0.195851217 -0.127152670 -0.280917019 -0.417820992 -0.951892077
37 38 39 40 41 42
-1.254701980 -1.200498610 -1.329943178 -0.668329174 -0.452480108 -0.664743752
43 44 45 46 47 48
-1.049810441 -1.111569402 -0.988068834 -0.561647552 -0.263410592 -0.010636869
49 50 51 52 53 54
-0.004840155 0.031245293 0.220106167 -0.123234201 -0.092624412 -0.268288079
55 56 57 58 59 60
-0.126478876 -0.181139440 -0.421879561 -0.263534201 -0.356792620 -0.338520256
61 62 63 64 65 66
-0.284325944 -0.083922211 0.176389378 0.093910944 -0.368330521 -0.130557353
67 68 69 70 71 72
0.016787521 0.028465854 -0.038451000 0.404389010 0.389558094 0.326523491
73 74 75 76 77 78
0.299598280 0.074566625 -0.034380860 -0.031891868 0.145314968 0.128022293
79 80 81 82 83 84
0.563765621 0.661102297 0.619428184 0.584157327 0.142100054 -0.041766984
85 86 87 88 89 90
-0.154666844 0.100922396 0.202618980 0.127914800 0.183889043 0.212382724
91 92 93 94 95 96
0.285230876 0.177674904 0.354457099 0.078924363 0.096604264 0.001885637
97 98 99 100 101 102
0.067815589 0.194895550 0.325582300 0.640742181 0.725725525 0.430800668
103 104 105 106 107 108
0.142658945 -0.214077896 -0.375881372 -0.428463176 -0.402573448 -0.080500605
109 110 111 112 113 114
-0.173574381 -0.427438851 -0.323249943 -0.104006038 -0.210412214 -0.523484028
115 116 117 118 119 120
-0.755185816 -0.975310817 -0.922649698 -0.613151086 -0.385613125 -0.278622479
121 122 123 124 125 126
-0.633015850 -0.775756769 -0.544470727 -0.457043714 0.032987137 0.539518820
127 128 129 130 131 132
0.673393000 0.372345883 0.351809384 0.211238328 0.717216440 0.051453705
133 134 135 136 137 138
-0.063909315 -0.074666805 -0.505369238 -0.513228423 -0.574017242 -0.828879945
139 140 141 142 143 144
-0.956657811 -0.797164061 -0.862937707 -0.915125198 -0.640357228 -0.363938618
145 146 147 148 149 150
-0.071351619 -0.043877159 0.048065635 -0.004383812 0.005391492 0.182722091
151 152 153 154
0.099100752 -0.309911029 -0.005849789 0.144672737
> postscript(file="/var/www/rcomp/tmp/68ljv1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 1.577269285 NA
1 1.505286785 1.577269285
2 1.684069115 1.505286785
3 1.814495152 1.684069115
4 1.607175538 1.814495152
5 1.521537471 1.607175538
6 1.403280906 1.521537471
7 1.258210174 1.403280906
8 1.078912834 1.258210174
9 0.950422696 1.078912834
10 0.664272430 0.950422696
11 0.554548714 0.664272430
12 0.390698874 0.554548714
13 0.558444595 0.390698874
14 0.715808408 0.558444595
15 0.427220242 0.715808408
16 0.459566489 0.427220242
17 0.819078000 0.459566489
18 0.662013645 0.819078000
19 0.619408933 0.662013645
20 0.629604465 0.619408933
21 0.404642265 0.629604465
22 0.303053912 0.404642265
23 0.292173960 0.303053912
24 -0.162198871 0.292173960
25 -0.047799863 -0.162198871
26 -0.333377943 -0.047799863
27 -0.286179106 -0.333377943
28 -0.483297980 -0.286179106
29 -0.447401719 -0.483297980
30 -0.815805169 -0.447401719
31 -0.195851217 -0.815805169
32 -0.127152670 -0.195851217
33 -0.280917019 -0.127152670
34 -0.417820992 -0.280917019
35 -0.951892077 -0.417820992
36 -1.254701980 -0.951892077
37 -1.200498610 -1.254701980
38 -1.329943178 -1.200498610
39 -0.668329174 -1.329943178
40 -0.452480108 -0.668329174
41 -0.664743752 -0.452480108
42 -1.049810441 -0.664743752
43 -1.111569402 -1.049810441
44 -0.988068834 -1.111569402
45 -0.561647552 -0.988068834
46 -0.263410592 -0.561647552
47 -0.010636869 -0.263410592
48 -0.004840155 -0.010636869
49 0.031245293 -0.004840155
50 0.220106167 0.031245293
51 -0.123234201 0.220106167
52 -0.092624412 -0.123234201
53 -0.268288079 -0.092624412
54 -0.126478876 -0.268288079
55 -0.181139440 -0.126478876
56 -0.421879561 -0.181139440
57 -0.263534201 -0.421879561
58 -0.356792620 -0.263534201
59 -0.338520256 -0.356792620
60 -0.284325944 -0.338520256
61 -0.083922211 -0.284325944
62 0.176389378 -0.083922211
63 0.093910944 0.176389378
64 -0.368330521 0.093910944
65 -0.130557353 -0.368330521
66 0.016787521 -0.130557353
67 0.028465854 0.016787521
68 -0.038451000 0.028465854
69 0.404389010 -0.038451000
70 0.389558094 0.404389010
71 0.326523491 0.389558094
72 0.299598280 0.326523491
73 0.074566625 0.299598280
74 -0.034380860 0.074566625
75 -0.031891868 -0.034380860
76 0.145314968 -0.031891868
77 0.128022293 0.145314968
78 0.563765621 0.128022293
79 0.661102297 0.563765621
80 0.619428184 0.661102297
81 0.584157327 0.619428184
82 0.142100054 0.584157327
83 -0.041766984 0.142100054
84 -0.154666844 -0.041766984
85 0.100922396 -0.154666844
86 0.202618980 0.100922396
87 0.127914800 0.202618980
88 0.183889043 0.127914800
89 0.212382724 0.183889043
90 0.285230876 0.212382724
91 0.177674904 0.285230876
92 0.354457099 0.177674904
93 0.078924363 0.354457099
94 0.096604264 0.078924363
95 0.001885637 0.096604264
96 0.067815589 0.001885637
97 0.194895550 0.067815589
98 0.325582300 0.194895550
99 0.640742181 0.325582300
100 0.725725525 0.640742181
101 0.430800668 0.725725525
102 0.142658945 0.430800668
103 -0.214077896 0.142658945
104 -0.375881372 -0.214077896
105 -0.428463176 -0.375881372
106 -0.402573448 -0.428463176
107 -0.080500605 -0.402573448
108 -0.173574381 -0.080500605
109 -0.427438851 -0.173574381
110 -0.323249943 -0.427438851
111 -0.104006038 -0.323249943
112 -0.210412214 -0.104006038
113 -0.523484028 -0.210412214
114 -0.755185816 -0.523484028
115 -0.975310817 -0.755185816
116 -0.922649698 -0.975310817
117 -0.613151086 -0.922649698
118 -0.385613125 -0.613151086
119 -0.278622479 -0.385613125
120 -0.633015850 -0.278622479
121 -0.775756769 -0.633015850
122 -0.544470727 -0.775756769
123 -0.457043714 -0.544470727
124 0.032987137 -0.457043714
125 0.539518820 0.032987137
126 0.673393000 0.539518820
127 0.372345883 0.673393000
128 0.351809384 0.372345883
129 0.211238328 0.351809384
130 0.717216440 0.211238328
131 0.051453705 0.717216440
132 -0.063909315 0.051453705
133 -0.074666805 -0.063909315
134 -0.505369238 -0.074666805
135 -0.513228423 -0.505369238
136 -0.574017242 -0.513228423
137 -0.828879945 -0.574017242
138 -0.956657811 -0.828879945
139 -0.797164061 -0.956657811
140 -0.862937707 -0.797164061
141 -0.915125198 -0.862937707
142 -0.640357228 -0.915125198
143 -0.363938618 -0.640357228
144 -0.071351619 -0.363938618
145 -0.043877159 -0.071351619
146 0.048065635 -0.043877159
147 -0.004383812 0.048065635
148 0.005391492 -0.004383812
149 0.182722091 0.005391492
150 0.099100752 0.182722091
151 -0.309911029 0.099100752
152 -0.005849789 -0.309911029
153 0.144672737 -0.005849789
154 NA 0.144672737
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.505286785 1.577269285
[2,] 1.684069115 1.505286785
[3,] 1.814495152 1.684069115
[4,] 1.607175538 1.814495152
[5,] 1.521537471 1.607175538
[6,] 1.403280906 1.521537471
[7,] 1.258210174 1.403280906
[8,] 1.078912834 1.258210174
[9,] 0.950422696 1.078912834
[10,] 0.664272430 0.950422696
[11,] 0.554548714 0.664272430
[12,] 0.390698874 0.554548714
[13,] 0.558444595 0.390698874
[14,] 0.715808408 0.558444595
[15,] 0.427220242 0.715808408
[16,] 0.459566489 0.427220242
[17,] 0.819078000 0.459566489
[18,] 0.662013645 0.819078000
[19,] 0.619408933 0.662013645
[20,] 0.629604465 0.619408933
[21,] 0.404642265 0.629604465
[22,] 0.303053912 0.404642265
[23,] 0.292173960 0.303053912
[24,] -0.162198871 0.292173960
[25,] -0.047799863 -0.162198871
[26,] -0.333377943 -0.047799863
[27,] -0.286179106 -0.333377943
[28,] -0.483297980 -0.286179106
[29,] -0.447401719 -0.483297980
[30,] -0.815805169 -0.447401719
[31,] -0.195851217 -0.815805169
[32,] -0.127152670 -0.195851217
[33,] -0.280917019 -0.127152670
[34,] -0.417820992 -0.280917019
[35,] -0.951892077 -0.417820992
[36,] -1.254701980 -0.951892077
[37,] -1.200498610 -1.254701980
[38,] -1.329943178 -1.200498610
[39,] -0.668329174 -1.329943178
[40,] -0.452480108 -0.668329174
[41,] -0.664743752 -0.452480108
[42,] -1.049810441 -0.664743752
[43,] -1.111569402 -1.049810441
[44,] -0.988068834 -1.111569402
[45,] -0.561647552 -0.988068834
[46,] -0.263410592 -0.561647552
[47,] -0.010636869 -0.263410592
[48,] -0.004840155 -0.010636869
[49,] 0.031245293 -0.004840155
[50,] 0.220106167 0.031245293
[51,] -0.123234201 0.220106167
[52,] -0.092624412 -0.123234201
[53,] -0.268288079 -0.092624412
[54,] -0.126478876 -0.268288079
[55,] -0.181139440 -0.126478876
[56,] -0.421879561 -0.181139440
[57,] -0.263534201 -0.421879561
[58,] -0.356792620 -0.263534201
[59,] -0.338520256 -0.356792620
[60,] -0.284325944 -0.338520256
[61,] -0.083922211 -0.284325944
[62,] 0.176389378 -0.083922211
[63,] 0.093910944 0.176389378
[64,] -0.368330521 0.093910944
[65,] -0.130557353 -0.368330521
[66,] 0.016787521 -0.130557353
[67,] 0.028465854 0.016787521
[68,] -0.038451000 0.028465854
[69,] 0.404389010 -0.038451000
[70,] 0.389558094 0.404389010
[71,] 0.326523491 0.389558094
[72,] 0.299598280 0.326523491
[73,] 0.074566625 0.299598280
[74,] -0.034380860 0.074566625
[75,] -0.031891868 -0.034380860
[76,] 0.145314968 -0.031891868
[77,] 0.128022293 0.145314968
[78,] 0.563765621 0.128022293
[79,] 0.661102297 0.563765621
[80,] 0.619428184 0.661102297
[81,] 0.584157327 0.619428184
[82,] 0.142100054 0.584157327
[83,] -0.041766984 0.142100054
[84,] -0.154666844 -0.041766984
[85,] 0.100922396 -0.154666844
[86,] 0.202618980 0.100922396
[87,] 0.127914800 0.202618980
[88,] 0.183889043 0.127914800
[89,] 0.212382724 0.183889043
[90,] 0.285230876 0.212382724
[91,] 0.177674904 0.285230876
[92,] 0.354457099 0.177674904
[93,] 0.078924363 0.354457099
[94,] 0.096604264 0.078924363
[95,] 0.001885637 0.096604264
[96,] 0.067815589 0.001885637
[97,] 0.194895550 0.067815589
[98,] 0.325582300 0.194895550
[99,] 0.640742181 0.325582300
[100,] 0.725725525 0.640742181
[101,] 0.430800668 0.725725525
[102,] 0.142658945 0.430800668
[103,] -0.214077896 0.142658945
[104,] -0.375881372 -0.214077896
[105,] -0.428463176 -0.375881372
[106,] -0.402573448 -0.428463176
[107,] -0.080500605 -0.402573448
[108,] -0.173574381 -0.080500605
[109,] -0.427438851 -0.173574381
[110,] -0.323249943 -0.427438851
[111,] -0.104006038 -0.323249943
[112,] -0.210412214 -0.104006038
[113,] -0.523484028 -0.210412214
[114,] -0.755185816 -0.523484028
[115,] -0.975310817 -0.755185816
[116,] -0.922649698 -0.975310817
[117,] -0.613151086 -0.922649698
[118,] -0.385613125 -0.613151086
[119,] -0.278622479 -0.385613125
[120,] -0.633015850 -0.278622479
[121,] -0.775756769 -0.633015850
[122,] -0.544470727 -0.775756769
[123,] -0.457043714 -0.544470727
[124,] 0.032987137 -0.457043714
[125,] 0.539518820 0.032987137
[126,] 0.673393000 0.539518820
[127,] 0.372345883 0.673393000
[128,] 0.351809384 0.372345883
[129,] 0.211238328 0.351809384
[130,] 0.717216440 0.211238328
[131,] 0.051453705 0.717216440
[132,] -0.063909315 0.051453705
[133,] -0.074666805 -0.063909315
[134,] -0.505369238 -0.074666805
[135,] -0.513228423 -0.505369238
[136,] -0.574017242 -0.513228423
[137,] -0.828879945 -0.574017242
[138,] -0.956657811 -0.828879945
[139,] -0.797164061 -0.956657811
[140,] -0.862937707 -0.797164061
[141,] -0.915125198 -0.862937707
[142,] -0.640357228 -0.915125198
[143,] -0.363938618 -0.640357228
[144,] -0.071351619 -0.363938618
[145,] -0.043877159 -0.071351619
[146,] 0.048065635 -0.043877159
[147,] -0.004383812 0.048065635
[148,] 0.005391492 -0.004383812
[149,] 0.182722091 0.005391492
[150,] 0.099100752 0.182722091
[151,] -0.309911029 0.099100752
[152,] -0.005849789 -0.309911029
[153,] 0.144672737 -0.005849789
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.505286785 1.577269285
2 1.684069115 1.505286785
3 1.814495152 1.684069115
4 1.607175538 1.814495152
5 1.521537471 1.607175538
6 1.403280906 1.521537471
7 1.258210174 1.403280906
8 1.078912834 1.258210174
9 0.950422696 1.078912834
10 0.664272430 0.950422696
11 0.554548714 0.664272430
12 0.390698874 0.554548714
13 0.558444595 0.390698874
14 0.715808408 0.558444595
15 0.427220242 0.715808408
16 0.459566489 0.427220242
17 0.819078000 0.459566489
18 0.662013645 0.819078000
19 0.619408933 0.662013645
20 0.629604465 0.619408933
21 0.404642265 0.629604465
22 0.303053912 0.404642265
23 0.292173960 0.303053912
24 -0.162198871 0.292173960
25 -0.047799863 -0.162198871
26 -0.333377943 -0.047799863
27 -0.286179106 -0.333377943
28 -0.483297980 -0.286179106
29 -0.447401719 -0.483297980
30 -0.815805169 -0.447401719
31 -0.195851217 -0.815805169
32 -0.127152670 -0.195851217
33 -0.280917019 -0.127152670
34 -0.417820992 -0.280917019
35 -0.951892077 -0.417820992
36 -1.254701980 -0.951892077
37 -1.200498610 -1.254701980
38 -1.329943178 -1.200498610
39 -0.668329174 -1.329943178
40 -0.452480108 -0.668329174
41 -0.664743752 -0.452480108
42 -1.049810441 -0.664743752
43 -1.111569402 -1.049810441
44 -0.988068834 -1.111569402
45 -0.561647552 -0.988068834
46 -0.263410592 -0.561647552
47 -0.010636869 -0.263410592
48 -0.004840155 -0.010636869
49 0.031245293 -0.004840155
50 0.220106167 0.031245293
51 -0.123234201 0.220106167
52 -0.092624412 -0.123234201
53 -0.268288079 -0.092624412
54 -0.126478876 -0.268288079
55 -0.181139440 -0.126478876
56 -0.421879561 -0.181139440
57 -0.263534201 -0.421879561
58 -0.356792620 -0.263534201
59 -0.338520256 -0.356792620
60 -0.284325944 -0.338520256
61 -0.083922211 -0.284325944
62 0.176389378 -0.083922211
63 0.093910944 0.176389378
64 -0.368330521 0.093910944
65 -0.130557353 -0.368330521
66 0.016787521 -0.130557353
67 0.028465854 0.016787521
68 -0.038451000 0.028465854
69 0.404389010 -0.038451000
70 0.389558094 0.404389010
71 0.326523491 0.389558094
72 0.299598280 0.326523491
73 0.074566625 0.299598280
74 -0.034380860 0.074566625
75 -0.031891868 -0.034380860
76 0.145314968 -0.031891868
77 0.128022293 0.145314968
78 0.563765621 0.128022293
79 0.661102297 0.563765621
80 0.619428184 0.661102297
81 0.584157327 0.619428184
82 0.142100054 0.584157327
83 -0.041766984 0.142100054
84 -0.154666844 -0.041766984
85 0.100922396 -0.154666844
86 0.202618980 0.100922396
87 0.127914800 0.202618980
88 0.183889043 0.127914800
89 0.212382724 0.183889043
90 0.285230876 0.212382724
91 0.177674904 0.285230876
92 0.354457099 0.177674904
93 0.078924363 0.354457099
94 0.096604264 0.078924363
95 0.001885637 0.096604264
96 0.067815589 0.001885637
97 0.194895550 0.067815589
98 0.325582300 0.194895550
99 0.640742181 0.325582300
100 0.725725525 0.640742181
101 0.430800668 0.725725525
102 0.142658945 0.430800668
103 -0.214077896 0.142658945
104 -0.375881372 -0.214077896
105 -0.428463176 -0.375881372
106 -0.402573448 -0.428463176
107 -0.080500605 -0.402573448
108 -0.173574381 -0.080500605
109 -0.427438851 -0.173574381
110 -0.323249943 -0.427438851
111 -0.104006038 -0.323249943
112 -0.210412214 -0.104006038
113 -0.523484028 -0.210412214
114 -0.755185816 -0.523484028
115 -0.975310817 -0.755185816
116 -0.922649698 -0.975310817
117 -0.613151086 -0.922649698
118 -0.385613125 -0.613151086
119 -0.278622479 -0.385613125
120 -0.633015850 -0.278622479
121 -0.775756769 -0.633015850
122 -0.544470727 -0.775756769
123 -0.457043714 -0.544470727
124 0.032987137 -0.457043714
125 0.539518820 0.032987137
126 0.673393000 0.539518820
127 0.372345883 0.673393000
128 0.351809384 0.372345883
129 0.211238328 0.351809384
130 0.717216440 0.211238328
131 0.051453705 0.717216440
132 -0.063909315 0.051453705
133 -0.074666805 -0.063909315
134 -0.505369238 -0.074666805
135 -0.513228423 -0.505369238
136 -0.574017242 -0.513228423
137 -0.828879945 -0.574017242
138 -0.956657811 -0.828879945
139 -0.797164061 -0.956657811
140 -0.862937707 -0.797164061
141 -0.915125198 -0.862937707
142 -0.640357228 -0.915125198
143 -0.363938618 -0.640357228
144 -0.071351619 -0.363938618
145 -0.043877159 -0.071351619
146 0.048065635 -0.043877159
147 -0.004383812 0.048065635
148 0.005391492 -0.004383812
149 0.182722091 0.005391492
150 0.099100752 0.182722091
151 -0.309911029 0.099100752
152 -0.005849789 -0.309911029
153 0.144672737 -0.005849789
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/71ciy1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/81ciy1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9blij1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10blij1292886715.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11xmyp1292886715.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12imfv1292886715.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1375u71292886715.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14aosv1292886715.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15wo911292886715.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16szsj1292886716.tab")
+ }
>
> try(system("convert tmp/1n2l81292886715.ps tmp/1n2l81292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ft2t1292886715.ps tmp/2ft2t1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ft2t1292886715.ps tmp/3ft2t1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/48ljv1292886715.ps tmp/48ljv1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/58ljv1292886715.ps tmp/58ljv1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/68ljv1292886715.ps tmp/68ljv1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/71ciy1292886715.ps tmp/71ciy1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/81ciy1292886715.ps tmp/81ciy1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/9blij1292886715.ps tmp/9blij1292886715.png",intern=TRUE))
character(0)
> try(system("convert tmp/10blij1292886715.ps tmp/10blij1292886715.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.480 1.440 5.926